A Machine Learning Approach for Heart Attack Prediction

Gour, Sanjay; Panwar, Punita; Dwivedi, Divya; Mali, Chetan

doi:10.1007/978-981-16-6309-3_70

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In the cataloguing of genetic cardiac illnesses and control subjects, a widespread set of ML algorithms with their variations is used to predict the early stages of heart failure [ 8 , 9 ]. KNN, DT, SVC, LR, and RF machine algorithms are examples of heart attack prediction algorithms [ 10 ]. Machine learning approaches can be divided into three categories [ 11 ]: Supervised ML: task drive, labeled data (classification/regression); Unsupervised ML: data-driven, unlabeled data (clustering); Reinforcement Learning: learning from mistakes (playing games).…”

Section: Introductionmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

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Section: Introductionmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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show abstract

“…According to a WHO report, cardiovascular diseases directly kill more than 17.8 million people every year. Because there isn't enough analysis, the healthcare industry's huge amounts of patient, illness, and diagnosis data don't have the effect on patient health that was hoped for 1 . Heart and blood vessel diseases, or CVDs, include coronary artery disease, myocarditis, vascular disease, and other conditions.…”

Section: Introductionmentioning

confidence: 99%

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database

Srinivasan,

Gunasekaran,

Mathivanan

et al. 2023

Sci Rep

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Heart disease is a significant global cause of mortality, and predicting it through clinical data analysis poses challenges. Machine learning (ML) has emerged as a valuable tool for diagnosing and predicting heart disease by analyzing healthcare data. Previous studies have extensively employed ML techniques in medical research for heart disease prediction. In this study, eight ML classifiers were utilized to identify crucial features that enhance the accuracy of heart disease prediction. Various combinations of features and well-known classification algorithms were employed to develop the prediction model. Neural network models, such as Naïve Bayes and Radial Basis Functions, were implemented, achieving accuracies of 94.78% and 90.78% respectively in heart disease prediction. Among the state-of-the-art methods for cardiovascular problem prediction, Learning Vector Quantization exhibited the highest accuracy rate of 98.7%. The motivation behind predicting Cardiovascular Heart Disease lies in its potential to save lives, improves health outcomes, and allocates healthcare resources efficiently. The key contributions encompass early intervention, personalized medicine, technological advancements, the impact on public health, and ongoing research, all of which collectively work toward reducing the burden of CHD on both individual patients and society as a whole.

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“…Akshit Rajesh Tayadeet al [7] used logistics regression algorithm for diet recommendation system to support mental fitness and physical fitness and accuracy of the proposed model was 85.96.…”

Section: Literature Surveymentioning

confidence: 99%

A Study on Calories Burnt Prediction Using Machine Learning

Panwar¹,

Bhutani²,

Sharma³

et al. 2023

ITM Web Conf.

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In this growing technological era, People are less aware of their health and mental stability. Due to lack of time, they intake more junk food than healthy options, which leads to an increase in the total calorie rate in their body. which is a major cause of obesity a calorie is the rate of energy stored and energy expenditure. People nowadays want quick solutions to every problem they want to exercise less and get more results, so to check the level of improvement and the burnt calories level after exercise in the human body we came up with this machine Learning System which takes some attributes as input and gives approximate calories burnt value which will motivate people to do more exercise and will show their daily growth The project is nourished with more than 15,000 data and its MAE (Mean Absolute error) is 1.48 which will enhance over time for better Results.

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A Machine Learning Approach for Heart Attack Prediction

Cited by 9 publications

References 6 publications

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database

A Study on Calories Burnt Prediction Using Machine Learning

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